Li Junning, Wang Z Jane, Palmer Samantha J, McKeown Martin J
Department of Electrical and Computer Engineering, University of British Columbia, Canada.
Neuroimage. 2008 Jun;41(2):398-407. doi: 10.1016/j.neuroimage.2008.01.068. Epub 2008 Mar 10.
Bayesian network (BN) modeling has recently been introduced as a tool for determining the dependencies between brain regions from functional-magnetic-resonance-imaging (fMRI) data. However, studies to date have yet to explore the optimum way for meaningfully combining individually determined BN models to make group inferences. We contrasted the results from three broad approaches: the "virtual-typical- subject" (VTS) approach which pools or averages group data as if they are sampled from a single, hypothetical virtual typical subject; the "individual-structure" (IS) approach that learns a separate BN for each subject, and then finds commonality across the individual structures, and the "common-structure" (CS) approach that imposes the same network structure on the BN of every subject, but allows the parameters to differ across subjects. To explore the effects of these three approaches, we applied them to an fMRI study exploring the motor effect of L-dopa medication on ten subjects with Parkinson's disease (PD), as the profound clinical effects of this medication suggest that fMRI activation in PD subjects after medication should start approaching that of age-matched controls. We found that none of these approaches is generally superior over the others, according to Bayesian-information-criterion (BIC) scores, and that they led to considerably different group-level results. The IS approach was more sensitive to the normalization effect of the L-dopa medication on brain connectivity. However, for the more homogeneous control population, the VTS approach was superior. Group-analysis approaches should be selected carefully with consideration of both statistical and biomedical evidence.
贝叶斯网络（BN）建模最近被引入，作为一种从功能磁共振成像（fMRI）数据确定脑区之间依赖性的工具。然而，迄今为止的研究尚未探索有意义地组合单独确定的BN模型以进行组推断的最佳方法。我们对比了三种主要方法的结果：“虚拟典型受试者”（VTS）方法，该方法汇总或平均组数据，就好像它们是从单个假设的虚拟典型受试者中采样的一样；“个体结构”（IS）方法，为每个受试者学习一个单独的BN，然后在个体结构中找到共性；以及“共同结构”（CS）方法，该方法对每个受试者的BN施加相同的网络结构，但允许参数在受试者之间有所不同。为了探究这三种方法的效果，我们将它们应用于一项fMRI研究，该研究探讨了左旋多巴药物对十名帕金森病（PD）患者的运动影响，因为这种药物的显著临床效果表明，用药后PD患者的fMRI激活应开始接近年龄匹配的对照组。我们发现，根据贝叶斯信息准则（BIC）得分，这些方法中没有一种在总体上优于其他方法，并且它们导致了截然不同的组水平结果。IS方法对左旋多巴药物对脑连接性的归一化作用更敏感。然而，对于更同质的对照人群，VTS方法更优越。应同时考虑统计和生物医学证据，谨慎选择组分析方法。
